In the project directory, you can run:
Assumes you have yarn, npm & pip installed
Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.
The page will reload if you make edits.
You will also see any lint errors in the console.
Note: If you get a warning about run-p not being found
Run npm i npm-run-all
Required npm installs:
npm install react
npm install react-router-dom
npm install d3
npm install socket.io-client
npm install react-dom
Launches the test runner in the interactive watch mode.
See the section about running tests for more information.
Builds the app for production to the build
folder.
It correctly bundles React in production mode and optimizes the build for the best performance.
The build is minified and the filenames include the hashes.
Your app is ready to be deployed!
See the section about deployment for more information.
Note: this is a one-way operation. Once you eject
, you can’t go back!
If you aren’t satisfied with the build tool and configuration choices, you can eject
at any time. This command will remove the single build dependency from your project.
Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except eject
will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.
You don’t have to ever use eject
. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.
Assumes python3 installed
Full list of commands (UNIX-BASED OS)
cd api
python3 -m venv venv
source venv/bin/activate
(Windows)
cd api
python -m venv venv
venv\Scripts\activate
(Instructions shared across OS):
pip install flask python-dotenv
pip install numpy
pip install flask-cors
pip install mne
pip install matplotlib
pip install tensorflow
pip install flask_socketio
pip install sklearn
Simulates a local version of the backend. Run this in another terminal window while running the front-end or the api calls won't work.